Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

This study found that hair canal and follicle diameters, outer root sheath area, and terminal-to-vellus hair ratios were significantly reduced in androgenetic alopecia patients compared to normal controls.

This study found a significant positive correlation between hair thickness and growth rate, with a notably slower growth rate observed in men with male pattern hair loss compared to healthy controls.

This review examines the use of computer vision techniques, specifically deep learning architectures and image processing algorithms, for detecting and assessing skin conditions like vitiligo and dermatitis, and highlights the need for disease-specific datasets to improve automated diagnostic tools in dermatology.

Photographic imaging is crucial for documenting and managing hair loss, requiring careful preparation and standardization to be effective.

This study reported that Trichoscan, a computer-assisted hair measurement system, was not supported as accurate for clinical trials due to errors in signal detection, despite its analysis speed.

This review discusses various methods for measuring scalp hair growth in androgenetic alopecia and reports no new results; the authors suggest that computer-assisted technology has untapped potential.

This article presents a computer imaging algorithm that may automate and enhance the Severity of Alopecia Tool scoring for alopecia areata through texture analysis of pediatric images.

This mini-review summarizes the Trichoscan as a sensitive tool for measuring hair growth parameters, demonstrating its effectiveness in detecting treatment response in androgenetic alopecia but noting some practical limitations.

This study demonstrated that using morphometric and 3D reconstruction software to evaluate scalp biopsies in non-cicatricial alopecias may be feasible and sensitive for detecting subtle follicular changes.

This dissertation proposes a framework for analyzing medical images on mobile devices but presents no new research findings, focusing instead on development methodologies and their applications to hair transplant and glaucoma contexts.

In this study, a new shading model for hair was developed that better matches real-life light scattering, thanks to detailed new measurements of hair fiber scattering not predicted by existing models.

This study introduced a new hair shading model that more accurately simulates light scattering on hair fibers, capturing effects not predicted by previous models.

This systematic review provides an in-depth evaluation of non-invasive imaging and biophysical methods for diagnosing and monitoring vitiligo, emphasizing the need for studies with larger sample sizes to verify their use in clinical practice and research.

This study concluded that TrichoScan is error-prone in its current form and overestimates certain hair growth parameters, with results not aligning with clinical severity of alopecia.

This letter compares automated digital image analysis (TrichoScan) with manual marking of hairs in male patients with androgenetic alopecia but reports no new results.

This study demonstrates that a new Adaptive Modulation and Coding method can improve control performance in Communication-Based Train Control systems using WLAN by reducing average delay through effective transmission mode selection.

This study reviews methods to ensure the reliability of machine learning models in medical imaging, focusing on bias detection, data drift assessment, and accuracy estimation without ground truth labels to enhance integration into clinical settings.

This study presents a new approach to automatically remove hair artifacts from dermoscopic images, which reportedly performed well compared to existing methods like DullRazor using the PH2 datasets.

In this scoping review, researchers observed that while AI-assisted trichoscopy holds promise for standardized assessments of hair and scalp disorders, its clinical translation is limited by small proprietary datasets, inconsistent validation protocols, and a scarcity of real-world clinical studies.

This study reports that the TrichoScan method effectively measures hair growth parameters and detected significant improvements in hair counts and thickness in men with androgenetic alopecia after finasteride treatment.

In this study, the researchers reported that their novel algorithm for hair restoration in dermoscopy images achieved high accuracy and texture preservation, outperforming other techniques in diagnostic accuracy and texture quality measures.

This study developed a deep learning framework and quantitative model that accurately predict basic and specific classification in male androgenetic alopecia by analyzing trichoscopic images.

This study assessed an AI-based mobile app for evaluating androgenetic alopecia severity, finding that it achieved 94% accuracy compared to human dermatologists, while participants often underestimated their own hair loss severity.

This retrospective study found that serum hormone levels and MRI detection of ovarian nodules contributed to distinguishing virilizing ovarian tumors from ovarian stromal hyperthecosis in postmenopausal women, though histopathology remains crucial for diagnosis.

Automated image analysis helps diagnose and monitor alopecia areata by efficiently measuring hair follicles.

This study used bibliometric analysis to evaluate global research on AI applications in dermatology, identifying 406 relevant papers and highlighting current priorities such as machine learning for wound progression, AI in teledermatology, and applications for skin diseases.

This study developed a high-resolution phototrichogram system that can automatically and accurately assess hair growth metrics in cosmetic trials, achieving over 90% correlation with manual measurements.

This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.

This study found that a hair tonic increased hair growth and reduced resting hair ratios in men with alopecia.